Short-term risk forecasts of heavy rainfall

Methodologies for risk forecasts of severe weather hardly exist on the scale of nowcasting (0–3 hours). Here we discuss short-term risk forecasts of heavy precipitation associated with local thunderstorms. We use COTREC/RainCast: a procedure to extrapolate radar images into the near future. An error density function is defined using the estimated error of location of the extrapolated radar patterns. The radar forecast is folded (“smeared”) with the density function, leading to a probability distribution of radar intensities. An algorithm to convert the radar intensities into values of precipitation intensity provides the desired probability (or risk) of heavy rainfall at any position within the considered window in space and time. We discuss, as an example, a flood event from summer 2000.

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Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

10.5194/egusphere-egu21-12814 ◽

2021 ◽

Author(s):

Eva van der Kooij ◽

Marc Schleiss ◽

Riccardo Taormina ◽

Francesco Fioranelli ◽

Dorien Lugt ◽

...

Keyword(s):

Machine Learning ◽

The Netherlands ◽

Heavy Rainfall ◽

Predictive Performance ◽

Heavy Precipitation ◽

Early Warning Systems ◽

Training Data ◽

Short Term ◽

Data Set ◽

Radar Images

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.

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Forecasting an Unusual Weather Event in Colorado: 15 October 1980

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477-63.10.1142 ◽

1982 ◽

Vol 63 (10) ◽

pp. 1142-1150 ◽

Cited By ~ 1

Author(s):

John F. Weaver ◽

John M. Brown

Keyword(s):

Heavy Rain ◽

Heavy Precipitation ◽

Severe Weather ◽

Time Data ◽

Short Term ◽

Weather Event ◽

Weather System ◽

Light Rain ◽

Term Forecast ◽

Surface Data

On 15 October 1980, a weather system that had been to the west of Colorado was forecast to move into the state, and to bring with it light to moderate snow in the Rockies, and generally light rain and thundershower activity over the plains to the east. In most regions this forecast was adequate. However, substantially heavier activity (including a small tornado, large hail, heavy rain, and snow) also occurred in some areas. In this paper we show how all relevant real-time data, when properly merged, could have enabled formulation of a useful short-term forecast. In addition we point out how mesonet surface data gathered after the fact could have helped narrow down the forecast area of severe weather and heavy precipitation.

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Study on the relationship between improved short-term precipitation forecast and insurance data for risk evaluation in Southern China

10.5194/egusphere-egu2020-19346 ◽

2020 ◽

Author(s):

Yue Zheng ◽

Ziye Zhou ◽

Cong Fang ◽

Jiaxi Liang ◽

Boyang Ren ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Risk Evaluation ◽

Heavy Rainfall ◽

Precipitation Forecast ◽

Precipitation Event ◽

Ensemble Model ◽

Short Term ◽

Radar Images ◽

Insurance Data

&#160; &#160; &#160; &#160; Heavy rainfall is one of the most frequent and severe weather hazards in the world which becomeone of the hugest natural risks.&#160; It has been found that during the flood season in South China, high intensive precipitation occurs very frequently due to the impact of east Asian monsoon. &#160;An unexpected and unusual extreme precipitation event could lead to millions or billions worth of damage, wash out vehicles and houses, destroy agricultural fields, and threat people&#8217;s lives.&#160; Determining the linkage between heavy rainfall causes, critical meteorological condition, and impacts can make it easier to classify risk level.&#160; However, due to the insufficiencies of quantitative heavy rainfall related property damages, and low efficient precipitation forecast, the risk evaluation could not be well determined.&#160; Therefore, we employed an improved short-term precipitation forecast based on ensemble deep learning algorithms that can provide more accurate prediction, and apply &#160;25 years of insurance data to aid as proxy for the evaluation of short-term heavy rainfall risks, aiming to trigger in-time precautions and reduce losses.&#160;&#160;&#160;&#160;&#160;&#160;&#160; The improved short-term precipitation forecast is built based on combination of scale-invariant feature transform (SIFT) algorithm and ensemble model including convolutional neural network (CNN), gradient boosting decision tree (GBDT), and neural network.&#160; The main dataset used includes radar images and station observed precipitation.&#160; The past 1.5 hour radar reflectivity images are measured at 15 times with an interval of 6 minutes, and in 4 different heights from 0.5 km to 3.5 km with an interval of 1 km.&#160; The hourly site precipitation is obtained from ground meteorology stations.&#160; The SIFT is used to calculate cloud trajectory velocity, and the CNN is implemented with features including pinpoint local radar images, spatial-temporal descriptions of the cloud movement and the global description of the cloud pattern.&#160; Weights are assigned to the ensemble model to compute the following 2-3 hours forecasting results.&#160; Additionally, the insurance data include more than 50 thousand records provided on a geography coordinate level for the last 25 years.&#160;&#160;&#160;&#160;&#160;&#160;&#160; Result shows that the insurance data have a strong correlation with short-term precipitation.&#160; It also indicates that our proposed model of short-term precipitation forecast outperforms only-deep learning-based and traditional optical flow-based methods.&#160; The insurance data could provide a good proxy for describing heavy rainfall damage and to aid to explore the causes and impacts.&#160; This study would greatly assist policy makers, civil protection agencies, and insurance companies to improve emergency systems and response mechanisms.

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Comparison between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports

Weather and Forecasting ◽

10.1175/waf-d-11-00070.1 ◽

2012 ◽

Vol 27 (3) ◽

pp. 684-699 ◽

Cited By ~ 40

Author(s):

Richard Dworak ◽

Kristopher Bedka ◽

Jason Brunner ◽

Wayne Feltz

Keyword(s):

United States ◽

Heavy Rainfall ◽

Situational Awareness ◽

Warm Season ◽

Heavy Precipitation ◽

Severe Weather ◽

High Temporal Resolution ◽

Eastern United States ◽

Severe Storm ◽

Convective Storms

Abstract Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-μm infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (<200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April–September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage.

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Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 Km Unified Model

10.20944/preprints202008.0295.v1 ◽

2020 ◽

Author(s):

Tshimbiluni Percy Muofhe ◽

Hector Chikoore ◽

Mary-Jane Bopape ◽

Nthaduleni S Nethengwe ◽

Thando Ndarana ◽

...

Keyword(s):

South Africa ◽

South African ◽

Heavy Rainfall ◽

Weather Forecasting ◽

Daily Rainfall ◽

Heavy Precipitation ◽

Unified Model ◽

Severe Weather ◽

Economic Activities ◽

Circulation Patterns

Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They often result in floods and snowfalls in winter disrupting economic activities. This paper examines the evolution and circulation patterns associated with severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against ECMWF’s ERA Interim reanalyses and GPM precipitation at 24-hour timesteps. We present five recent (2016-2019) severe COLs that had high impact and found higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM underestimated precipitation mainly due to inaccurate placing of COL centers and areas of heavy rainfall by up to 5° of latitude away from the actual location, due to the poor formulating of cumulus and microphysics schemes in the model. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.

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Decline in Marriage Associated with the COVID-19 Pandemic in the United States

Socius Sociological Research for a Dynamic World ◽

10.1177/2378023120980328 ◽

2020 ◽

Vol 6 ◽

pp. 237802312098032

Author(s):

Brandon G. Wagner ◽

Kate H. Choi ◽

Philip N. Cohen

Keyword(s):

United States ◽

The United States ◽

Cumulative Number ◽

Short Term ◽

Know How ◽

The Social ◽

Reporting Delays ◽

Social Upheaval ◽

Near Future ◽

Do So

In the social upheaval arising from the coronavirus disease 2019 (COVID-19) pandemic, we do not yet know how union formation, particularly marriage, has been affected. Using administration records—marriage certificates and applications—gathered from settings representing a variety of COVID-19 experiences in the United States, the authors compare counts of recorded marriages in 2020 against those from the same period in 2019. There is a dramatic decrease in year-to-date cumulative marriages in 2020 compared with 2019 in each case. Similar patterns are observed for the Seattle metropolitan area when analyzing the cumulative number of marriage applications, a leading indicator of marriages in the near future. Year-to-date declines in marriage are unlikely to be due solely to closure of government agencies that administer marriage certification or reporting delays. Together, these findings suggest that marriage has declined during the COVID-19 outbreak and may continue to do so, at least in the short term.

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Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

ACM Transactions on Internet Technology ◽

10.1145/3447513 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1-28

Author(s):

Song Deng ◽

Fulin Chen ◽

Xia Dong ◽

Guangwei Gao ◽

Xindong Wu

Keyword(s):

Gene Expression ◽

Probability Distribution ◽

Detection Rate ◽

Cross Validation ◽

Normal Load ◽

Gene Expression Programming ◽

Load Forecasting ◽

Short Term ◽

Forecasting Models ◽

Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

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Effects of strategic tillage on short-term erosion, nutrient loss in runoff and greenhouse gas emissions

Soil Research ◽

10.1071/sr16136 ◽

2017 ◽

Vol 55 (3) ◽

pp. 201 ◽

Cited By ~ 16

Author(s):

A. R. Melland ◽

D. L. Antille ◽

Y. P. Dang

Keyword(s):

Nitrous Oxide ◽

Greenhouse Gas ◽

Heavy Rainfall ◽

Ghg Emissions ◽

Nutrient Loss ◽

Nitrous Oxide Emissions ◽

Nutrient Losses ◽

Short Term ◽

Trade Offs ◽

Carbon Dioxide Co2

Occasional strategic tillage (ST) of long-term no-tillage (NT) soil to help control weeds may increase the risk of water, erosion and nutrient losses in runoff and of greenhouse gas (GHG) emissions compared with NT soil. The present study examined the short-term effect of ST on runoff and GHG emissions in NT soils under controlled-traffic farming regimes. A rainfall simulator was used to generate runoff from heavy rainfall (70mmh–1) on small plots of NT and ST on a Vertosol, Dermosol and Sodosol. Nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) fluxes from the Vertosol and Sodosol were measured before and after the rain using passive chambers. On the Sodosol and Dermosol there was 30% and 70% more runoff, respectively, from ST plots than from NT plots, however, volumes were similar between tillage treatments on the Vertosol. Erosion was highest after ST on the Sodosol (8.3tha–1 suspended sediment) and there were no treatment differences on the other soils. Total nitrogen (N) loads in runoff followed a similar pattern, with 10.2kgha–1 in runoff from the ST treatment on the Sodosol. Total phosphorus loads were higher after ST than NT on both the Sodosol (3.1 and 0.9kgha–1, respectively) and the Dermosol (1.0 and 0.3kgha–1, respectively). Dissolved nutrient forms comprised less than 13% of total losses. Nitrous oxide emissions were low from both NT and ST in these low-input systems. However, ST decreased CH4 absorption from both soils and almost doubled CO2 emissions from the Sodosol. Strategic tillage may increase the susceptibility of Sodosols and Dermosols to water, sediment and nutrient losses in runoff after heavy rainfall. The trade-offs between weed control, erosion and GHG emissions should be considered as part of any tillage strategy.

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